Kirjahaku

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive modelCreate univariate forecasting models that account for seasonal effects and external variablesBuild multivariate forecasting models to predict many time series at onceLeverage large datasets by using deep learning for forecasting time seriesAutomate the forecasting process DESCRIPTION Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. about the technology Time series forecasting reveals hidden trends and makes predictions about the future from your data. This powerful technique has proven incredibly valuable across multiple fields—from tracking business metrics, to healthcare and the sciences. Modern Python libraries and powerful deep learning tools have opened up new methods and utilities for making practical time series forecasts. about the book Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. By the time you're done, you'll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.

Your forecasts lag while data grows, hardware costs, and deadlines tighten. Traditional model training demands weeks of tuning and GPU burns time. Meanwhile, foundation models already understand seasonality, holiday spikes, and rare shocks. This book hands you TimeGPT, Chronos, and other pretrained powerhouses. Generate zero-shot forecasts or fine-tune quickly with only laptop resources. Deliver stronger predictions, faster insights, and measurable business value in days, not months. Model internals explained: Understand how large time models capture temporal patterns and uncertainty. Zero-shot workflow: Run instant forecasts on custom data without retraining, saving weeks of effort. Fine-tuning guides: Adapt foundation models to niche domains for even higher accuracy. Evaluation playbook: Benchmark probabilistic and point forecasts using industry-standard metrics. Laptop-friendly code: All examples rely on Python and CPUs, no high-end GPUs required. Time Series Forecasting Using Foundation Models by data-science instructor Marco Peixeiro containing clear diagrams, annotated notebooks, and rigorously tested examples establish immediate credibility. You build a tiny foundation model to grasp pretraining mechanics, then experiment with production-grade models like TimeGPT and Chronos. Each chapter layers hands-on labs, checkpoints, and real-world case studies. Finish ready to integrate pretrained forecasting models, slash development time, and present trustworthy predictions to stakeholders. Your pipeline becomes faster, cheaper, and easier to maintain. Designed for data scientists and ML engineers comfortable with basic forecasting theory and Python.